The remaining numerical hurdles at this stage are related to the mixed optimization variables and the non-convex objective problem. Next, we tackle the objective function using FP, in particular, the quadratic transform theorem [1]}. Although (REF ) has a sum-of-log-of-ratio form, we argue that because the main component of the objective function is a fraction (i.e., the SINR term in the \(R_{u, q}\) ), we can apply FP. Particularly, we apply multidimensional and complex FP quadratic transform [1]}.
